code: def recommend(
      algo, users, n, candidates=None, *, n_jobs=None, **kwargs
  ):
      """
      Batch-recommend for multiple users.  The provided algorithm should be a
      :py:class:`algorithms.Recommender`.
  
      Args:
          algo: the algorithm
          users(array-like): the users to recommend for
          n(int): the number of recommendations to generate (None for unlimited)
          candidates:
              the users' candidate sets. This can be a function, in which case it will
              be passed each user ID; it can also be a dictionary, in which case user
              IDs will be looked up in it.  Pass ``None`` to use the recommender's
              built-in candidate selector (usually recommended).
          n_jobs(int):
              The number of processes to use for parallel recommendations.  Passed to
              :func:`lenskit.util.parallel.invoker`.
  
      Returns:
          A frame with at least the columns ``user``, ``rank``, and ``item``; possibly also
          ``score``, and any other columns returned by the recommender.
      """
  
      if n_jobs is None and "nprocs" in kwargs:
          n_jobs = kwargs["nprocs"]
          warnings.warn(
              "nprocs is deprecated, use n_jobs", DeprecationWarning
          )
  
      if not isinstance(algo, PersistedModel):
          rec_algo = Recommender.adapt(algo)
          if candidates is None and rec_algo is not algo:
              warnings.warn(
                  "no candidates provided and algo is not a recommender, unlikely to work"
              )
          algo = rec_algo
          del rec_algo
  
      if "ratings" in kwargs:
          warnings.warn(
              "Providing ratings to recommend is not supported",
              DeprecationWarning,
          )
  
      candidates = __standard_cand_fun(candidates)
  
      with util.parallel.invoker(
          algo, _recommend_user, n_jobs=n_jobs
      ) as worker:
          _logger.info(
              "recommending with %s for %d users (n_jobs=%s)",
              str(algo),
              len(users),
              n_jobs,
          )
          del algo
          timer = util.Stopwatch()
          results = worker.map(
              (user, n, candidates(user)) for user in users
          )
          results = pd.concat(results, ignore_index=True, copy=False)
          _logger.info(
              "recommended for %d users in %s", len(users), timer
          )
  
      return results
  
file_path: lkpy/lenskit/batch/_recommend.py
imports: import logging
  import warnings
  import pandas
  import numpy
  from ..algorithms import Recommender
  from ..None import util
  from ..sharing import PersistedModel
label_desc: Use image node_id 3 for calling a global function with example usage: recommend(algo, users, n, candidates) and returns: results